Fit a boosting model to the training set with Purchase as the response and the other variables as predictors. Use 1,000 trees, and a shrinkage value of 0.01. Which predictors appear to be the most important

Answer and Explanation:

For the training set consist of 1000 observations, let's have a set consist of the observations such as sets.seed train boost.caravan gbm(Purchase., data = Caravan.train , distribution = "gaussian" , n trees = 1000 summary(boost.caravan) The tree classification such as tree(formula = Purchase data - train where variable are used in tree construction and number of terminal nodes 0 and deviance is 0.7 and error is 0.165

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